Triple
T4277870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RStudio |
E97084
|
entity |
| Predicate | hasEdition |
P35
|
FINISHED |
| Object | RStudio Server |
E97084
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: RStudio Server | Statement: [RStudio, hasEdition, RStudio Server]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RStudio Server Context triple: [RStudio, hasEdition, RStudio Server]
-
A.
RStudio
chosen
RStudio is an integrated development environment (IDE) for the R programming language, widely used for data analysis, visualization, and statistical computing.
-
B.
Streamlit Community Cloud
Streamlit Community Cloud is a hosted platform that lets users easily deploy, share, and manage Streamlit data apps directly from their code repositories.
-
C.
R Foundation for Statistical Computing
The R Foundation for Statistical Computing is a non-profit organization that supports the development, maintenance, and promotion of the R programming language and its ecosystem.
-
D.
Aqua Data Studio
Aqua Data Studio is a database management and development environment that provides tools for querying, visualizing, and administering a wide range of relational and NoSQL databases.
-
E.
JupyterLab
JupyterLab is a web-based interactive development environment for working with Jupyter notebooks, code, and data.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c7237b608190ab5aca56027344c4 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 12, 2026, 11:07 p.m.